FACE RECOGNITION¶

Context

Company X owns a movie application and repository that caters to movie streaming to millions of users on a subscription basis. The company wants to automate the process of cast and crew information in each scene from a movie such that when a user pauses on the movie and clicks on the cast information button, the app will show details of the actor in the scene. The company has in-house computer vision and multimedia experts who need to detect faces from screenshots of the movie scene.

Objective

Part A: To build a face detection system

Part B: To create an image dataset to be used by the AI team to build image classifier data

Part C: To build a face recognition system

Part A: To build a face detection system

Part I: Data Preprocessing and Visualization

In [1]:
#Reading the training images from the path and labelling them into the given categories
import numpy as np
import pandas as pd
import keras
import cv2
import os
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import seaborn as sns # for data visualization
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input
from google.colab.patches import cv2_imshow
In [2]:
from google.colab import drive
drive.mount('/content/drive')

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
In [3]:
#Step 1: Import the Data
image_data = np.load('/content/drive/MyDrive/FaceRecognition/Images.npy', allow_pickle=True)
In [4]:
print(image_data.shape)  # Check dimensions
print(type(image_data))  # Check type

(393, 2)
<class 'numpy.ndarray'>
In [5]:
# Step 2: Split images and masks
images = []
masks = []
for img, metadata in image_data:
    images.append(img)
    metadata_dict = metadata[0]  # Access the dictionary
    masks.append(metadata_dict)
In [6]:
# Step 3: Resize images and masks
# Define target dimensions
target_size = (128, 128)

resized_images = []
resized_masks = []
In [7]:
for img, metadata in zip(images, masks):
    # Resize image
    resized_img = cv2.resize(img, target_size)
    resized_images.append(resized_img)

    # Create a blank mask
    mask = np.zeros(target_size, dtype=np.uint8)
    img_width, img_height = metadata['imageWidth'], metadata['imageHeight']

    # Extract points and resize mask
    points = metadata['points']
    x_min = int(points[0]['x'] * target_size[0])
    y_min = int(points[0]['y'] * target_size[1])
    x_max = int(points[1]['x'] * target_size[0])
    y_max = int(points[1]['y'] * target_size[1])

    # Draw rectangle mask
    mask[y_min:y_max, x_min:x_max] = 255
    resized_masks.append(mask)
In [8]:
# Step 4: Visualize original and masked images
def visualize(img, mask, title):
    plt.figure(figsize=(10, 5))
    plt.subplot(1, 2, 1)
    plt.imshow(img)
    plt.title(f"Original Image: {title}")
    plt.axis('off')

    plt.subplot(1, 2, 2)
    plt.imshow(img)
    plt.imshow(mask, cmap='jet', alpha=0.5)  # Overlay mask with transparency
    plt.title(f"Masked Image: {title}")
    plt.axis('off')

    plt.show()

# Visualize first 5 images
for i in range(5):
    visualize(resized_images[i], resized_masks[i], f"Image {i+1}")
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In [9]:
#Prepare the data before spliting
standardized_images = []
for img in resized_images:
    # Check if the image has 2 or 3 dimensions
    if len(img.shape) == 2:  # Grayscale
        # Convert grayscale to RGB by duplicating the channels
        img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
    elif len(img.shape) == 3 and img.shape[2] == 4:  # RGBA
        # Remove the alpha channel
        img = img[:, :, :3]

    # Add the standardized image to the list
    standardized_images.append(img)

# Convert to a NumPy array
X = np.array(standardized_images)
print(f"Shape of X: {X.shape}")

Shape of X: (393, 128, 128, 3)
In [10]:
print(f"Final NumPy Array Shape: {X.shape}")

Final NumPy Array Shape: (393, 128, 128, 3)
In [11]:
standardized_masks = []
for mask in resized_masks:
    # Check the dimensionality of the mask
    if len(mask.shape) == 3 and mask.shape[2] == 1:  # If mask has a singleton channel
        mask = np.squeeze(mask, axis=2)  # Remove the singleton dimension
    elif len(mask.shape) == 3:  # If mask has multiple channels, keep only the first
        mask = mask[:, :, 0]

    # Resize the mask to the target size
    standardized_mask = cv2.resize(mask, target_size, interpolation=cv2.INTER_NEAREST)

    # Ensure mask values are 0 or 255 (binary masks)
    standardized_mask = (standardized_mask > 127).astype('uint8') * 255

    # Add to the list of standardized masks
    standardized_masks.append(standardized_mask)

# Convert to a NumPy array
y = np.array(standardized_masks)

# Check the final shapes
print(f"Final Mask Array Shape: {y.shape}")

Final Mask Array Shape: (393, 128, 128)
In [12]:
#Step 5: Split data into train and test

X = np.array(standardized_images)
y = np.array(standardized_masks)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Display dataset shapes
print(f"Train Images: {X_train.shape}, Train Masks: {y_train.shape}")
print(f"Test Images: {X_test.shape}, Test Masks: {y_test.shape}")

Train Images: (314, 128, 128, 3), Train Masks: (314, 128, 128)
Test Images: (79, 128, 128, 3), Test Masks: (79, 128, 128)
In [13]:
def visualize_sample(image, mask, title="Sample"):
    plt.figure(figsize=(10, 5))
    plt.subplot(1, 2, 1)
    plt.imshow(image)
    plt.title(f"{title}: Original Image")
    plt.axis('off')

    plt.subplot(1, 2, 2)
    plt.imshow(mask, cmap='gray')
    plt.title(f"{title}: Corresponding Mask")
    plt.axis('off')
    plt.show()

# Visualize a few train and test samples
for i in range(2):
    visualize_sample(X_train[i], y_train[i], title=f"Train Sample {i+1}")
    visualize_sample(X_test[i], y_test[i], title=f"Test Sample {i+1}")
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In [14]:
X_train = X_train / 255.0
X_test = X_test / 255.0
In [15]:
# Example Simple U-Net-like Model
def build_model(input_shape):
    model = Sequential()
    model.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=input_shape))
    model.add(MaxPooling2D((2, 2), padding='same'))
    model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
    model.add(MaxPooling2D((2, 2), padding='same'))
    model.add(UpSampling2D((2, 2)))
    model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
    model.add(UpSampling2D((2, 2)))
    model.add(Conv2D(1, (3, 3), activation='sigmoid', padding='same'))  # Binary mask output
    return model

model = build_model((128, 128, 3))
model.summary()

/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ conv2d (Conv2D)                      │ (None, 128, 128, 64)        │           1,792 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ max_pooling2d (MaxPooling2D)         │ (None, 64, 64, 64)          │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ conv2d_1 (Conv2D)                    │ (None, 64, 64, 128)         │          73,856 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ max_pooling2d_1 (MaxPooling2D)       │ (None, 32, 32, 128)         │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ up_sampling2d (UpSampling2D)         │ (None, 64, 64, 128)         │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ conv2d_2 (Conv2D)                    │ (None, 64, 64, 64)          │          73,792 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ up_sampling2d_1 (UpSampling2D)       │ (None, 128, 128, 64)        │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ conv2d_3 (Conv2D)                    │ (None, 128, 128, 1)         │             577 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 150,017 (586.00 KB)

 Trainable params: 150,017 (586.00 KB)

 Non-trainable params: 0 (0.00 B)

Part I: Model Building and Evaluation

  1. Design a Face Mask Detection Model
In [16]:
def build_unet(input_shape):
    inputs = Input(input_shape)

    # Encoder
    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
    pool1 = MaxPooling2D((2, 2))(conv1)

    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool1)
    pool2 = MaxPooling2D((2, 2))(conv2)

    # Bottleneck
    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool2)

    # Decoder
    up4 = UpSampling2D((2, 2))(conv3)
    up4 = concatenate([up4, conv2])
    conv4 = Conv2D(128, (3, 3), activation='relu', padding='same')(up4)

    up5 = UpSampling2D((2, 2))(conv4)
    up5 = concatenate([up5, conv1])
    conv5 = Conv2D(64, (3, 3), activation='relu', padding='same')(up5)

    # Output layer
    outputs = Conv2D(1, (1, 1), activation='sigmoid')(conv5)

    model = Model(inputs, outputs)
    return model

# Build the model
input_shape = (128, 128, 3)
model = build_unet(input_shape)
model.summary()

Model: "functional_8"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Layer (type)              ┃ Output Shape           ┃        Param # ┃ Connected to           ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
│ input_layer_1             │ (None, 128, 128, 3)    │              0 │ -                      │
│ (InputLayer)              │                        │                │                        │
├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤
│ conv2d_4 (Conv2D)         │ (None, 128, 128, 64)   │          1,792 │ input_layer_1[0][0]    │
├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤
│ max_pooling2d_2           │ (None, 64, 64, 64)     │              0 │ conv2d_4[0][0]         │
│ (MaxPooling2D)            │                        │                │                        │
├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤
│ conv2d_5 (Conv2D)         │ (None, 64, 64, 128)    │         73,856 │ max_pooling2d_2[0][0]  │
├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤
│ max_pooling2d_3           │ (None, 32, 32, 128)    │              0 │ conv2d_5[0][0]         │
│ (MaxPooling2D)            │                        │                │                        │
├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤
│ conv2d_6 (Conv2D)         │ (None, 32, 32, 256)    │        295,168 │ max_pooling2d_3[0][0]  │
├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤
│ up_sampling2d_2           │ (None, 64, 64, 256)    │              0 │ conv2d_6[0][0]         │
│ (UpSampling2D)            │                        │                │                        │
├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤
│ concatenate (Concatenate) │ (None, 64, 64, 384)    │              0 │ up_sampling2d_2[0][0], │
│                           │                        │                │ conv2d_5[0][0]         │
├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤
│ conv2d_7 (Conv2D)         │ (None, 64, 64, 128)    │        442,496 │ concatenate[0][0]      │
├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤
│ up_sampling2d_3           │ (None, 128, 128, 128)  │              0 │ conv2d_7[0][0]         │
│ (UpSampling2D)            │                        │                │                        │
├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤
│ concatenate_1             │ (None, 128, 128, 192)  │              0 │ up_sampling2d_3[0][0], │
│ (Concatenate)             │                        │                │ conv2d_4[0][0]         │
├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤
│ conv2d_8 (Conv2D)         │ (None, 128, 128, 64)   │        110,656 │ concatenate_1[0][0]    │
├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤
│ conv2d_9 (Conv2D)         │ (None, 128, 128, 1)    │             65 │ conv2d_8[0][0]         │
└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘

 Total params: 924,033 (3.52 MB)

 Trainable params: 924,033 (3.52 MB)

 Non-trainable params: 0 (0.00 B)

2. Compile and Train the Model

In [17]:
X_train = X_train / 255.0  # Normalize images
X_test = X_test / 255.0

y_train = y_train / 255.0  # Normalize masks to 0-1 range
y_test = y_test / 255.0
In [21]:
# Compile the model
model.compile(optimizer=Adam(learning_rate=1e-4), loss='binary_crossentropy', metrics=['accuracy'])

# Train the model
history = model.fit(
    X_train, y_train,
    validation_data=(X_test, y_test),
    batch_size=16,
    epochs=20
)

Epoch 1/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 253s 12s/step - accuracy: 0.7932 - loss: 0.6878 - val_accuracy: 0.9168 - val_loss: 0.6423
Epoch 2/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 248s 12s/step - accuracy: 0.9358 - loss: 0.5665 - val_accuracy: 0.9168 - val_loss: 0.3027
Epoch 3/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 268s 12s/step - accuracy: 0.9379 - loss: 0.2639 - val_accuracy: 0.9168 - val_loss: 0.3025
Epoch 4/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 243s 12s/step - accuracy: 0.9322 - loss: 0.2642 - val_accuracy: 0.9168 - val_loss: 0.3113
Epoch 5/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 264s 12s/step - accuracy: 0.9385 - loss: 0.2457 - val_accuracy: 0.9168 - val_loss: 0.3011
Epoch 6/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 257s 12s/step - accuracy: 0.9289 - loss: 0.2710 - val_accuracy: 0.9168 - val_loss: 0.3104
Epoch 7/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 265s 12s/step - accuracy: 0.9319 - loss: 0.2641 - val_accuracy: 0.9168 - val_loss: 0.3028
Epoch 8/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 263s 12s/step - accuracy: 0.9360 - loss: 0.2510 - val_accuracy: 0.9168 - val_loss: 0.3009
Epoch 9/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 237s 12s/step - accuracy: 0.9278 - loss: 0.2722 - val_accuracy: 0.9168 - val_loss: 0.3069
Epoch 10/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 231s 12s/step - accuracy: 0.9367 - loss: 0.2478 - val_accuracy: 0.9168 - val_loss: 0.2974
Epoch 11/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 270s 12s/step - accuracy: 0.9362 - loss: 0.2486 - val_accuracy: 0.9168 - val_loss: 0.2984
Epoch 12/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 252s 12s/step - accuracy: 0.9338 - loss: 0.2528 - val_accuracy: 0.9168 - val_loss: 0.2947
Epoch 13/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 227s 11s/step - accuracy: 0.9309 - loss: 0.2588 - val_accuracy: 0.9168 - val_loss: 0.2943
Epoch 14/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 265s 12s/step - accuracy: 0.9355 - loss: 0.2452 - val_accuracy: 0.9168 - val_loss: 0.2910
Epoch 15/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 256s 11s/step - accuracy: 0.9358 - loss: 0.2427 - val_accuracy: 0.9168 - val_loss: 0.2876
Epoch 16/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 269s 12s/step - accuracy: 0.9328 - loss: 0.2479 - val_accuracy: 0.9168 - val_loss: 0.2892
Epoch 17/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 260s 11s/step - accuracy: 0.9298 - loss: 0.2547 - val_accuracy: 0.9168 - val_loss: 0.2843
Epoch 18/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 261s 11s/step - accuracy: 0.9288 - loss: 0.2536 - val_accuracy: 0.9168 - val_loss: 0.2885
Epoch 19/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 241s 12s/step - accuracy: 0.9284 - loss: 0.2563 - val_accuracy: 0.9168 - val_loss: 0.2786
Epoch 20/20
20/20 ━━━━━━━━━━━━━━━━━━━━ 263s 12s/step - accuracy: 0.9303 - loss: 0.2471 - val_accuracy: 0.9168 - val_loss: 0.2784
In [22]:
import matplotlib.pyplot as plt

# Plot training history
plt.figure(figsize=(10, 5))

# Loss
plt.subplot(1, 2, 1)
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.title('Loss')
plt.legend()

# Accuracy
plt.subplot(1, 2, 2)
plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.title('Accuracy')
plt.legend()

plt.show()
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3. Evaluate and Share Insights

In [23]:
# Evaluate the model on the test set
test_loss, test_accuracy = model.evaluate(X_test, y_test)
print(f"Test Loss: {test_loss}")
print(f"Test Accuracy: {test_accuracy}")

3/3 ━━━━━━━━━━━━━━━━━━━━ 16s 4s/step - accuracy: 0.9148 - loss: 0.2829
Test Loss: 0.27843499183654785
Test Accuracy: 0.9167796969413757
In [24]:
#Visualize Training Progress
import matplotlib.pyplot as plt

# Plot training history
plt.figure(figsize=(10, 5))

# Loss
plt.subplot(1, 2, 1)
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.title('Loss')
plt.legend()

# Accuracy
plt.subplot(1, 2, 2)
plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.title('Accuracy')
plt.legend()

plt.show()
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4. Predict and Visualize Masks for Test Images

In [38]:
# Predict masks for the test images
predicted_masks = model.predict(X_test)

# Threshold the predictions to binary values
predicted_masks = (predicted_masks > 0.5).astype('uint8')

# Visualize predictions
def visualize_prediction(image, true_mask, predicted_mask, title="Sample"):
    plt.figure(figsize=(15, 5))

    plt.subplot(1, 3, 1)
    plt.imshow(image)
    plt.title(f"{title}: Original Image")
    plt.axis('off')

    plt.subplot(1, 3, 2)
    plt.imshow(true_mask, cmap='gray')
    plt.title(f"{title}: True Mask")
    plt.axis('off')

    plt.subplot(1, 3, 3)
    plt.imshow(predicted_mask, cmap='gray')
    plt.title(f"{title}: Predicted Mask")
    plt.axis('off')

    plt.show()

# Visualize the first 5 test predictions
for i in range(5):
    visualize_prediction(X_test[i], y_test[i], predicted_masks[i], title=f"Test Image {i+1}")

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Part I: Predicting Face Masks from Images

In [39]:
#1. Import Images from Folder

# Define the folder path
folder_path = '/content/drive/MyDrive/FaceRecognition/training_images-20211126T092819Z-001/training_images'

# Read all image file paths
image_paths = [os.path.join(folder_path, fname) for fname in os.listdir(folder_path) if fname.endswith(('.jpg', '.png', '.jpeg'))]
print(f"Found {len(image_paths)} images.")

Found 1091 images.
In [40]:
#2. Detect Faces and Extract Metadata
# Load Haar Cascade for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Initialize a list for storing metadata
metadata = []

# Process each image
for img_path in image_paths:
    # Read the image
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # Convert to grayscale for face detection

    # Detect faces
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    # Collect metadata for each detected face
    for (x, y, w, h) in faces:
        metadata.append({
            'image_path': img_path,
            'face_x': x,
            'face_y': y,
            'face_width': w,
            'face_height': h,
            'image_width': img.shape[1],
            'image_height': img.shape[0]
        })
In [41]:
#3. Save Metadata into a DataFrame
#Convert the metadata into a pandas DataFrame and save it as a CSV file.
# Create a DataFrame from metadata
df = pd.DataFrame(metadata)

# Save the DataFrame to a CSV file
output_csv = 'face_metadata.csv'
df.to_csv(output_csv, index=False)
print(f"Metadata saved to {output_csv}.")

Metadata saved to face_metadata.csv.
In [42]:
#4. Visualize Detected Faces
#Display a few images with the detected faces for verification.
import matplotlib.pyplot as plt

# Function to visualize faces
def visualize_faces(img_path, faces):
    img = cv2.imread(img_path)
    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)  # Draw rectangle around face

    plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    plt.axis('off')
    plt.show()

# Visualize the first 5 images
for i, img_path in enumerate(image_paths[:5]):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
    visualize_faces(img_path, faces)
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Part B: To create an image dataset to be used by the AI team to build image classifier data

Part II: Data Preprocessing

Step 1: Import the Data PINS.zip

In [50]:
#1. Unzip and Inspect the Dataset
import zipfile
import os
from zipfile import ZipFile

# Path to the ZIP file
zip_path = '/content/drive/MyDrive/FaceRecognition/PINS/PINS.zip'
extract_path = '/content/drive/MyDrive/FaceRecognition/PINS/PINS_dataset'

# Create directory if it doesn't exist
os.makedirs(extract_path, exist_ok=True)

# Extract zip file
with ZipFile(zip_path, 'r') as zip_ref:
    zip_ref.extractall(extract_path)
print(f"Data extracted to: {extract_path}")

Data extracted to: /content/drive/MyDrive/FaceRecognition/PINS/PINS_dataset/PINS
In [51]:
#2. Inspect Extracted Files

extract_path = '/content/drive/MyDrive/FaceRecognition/PINS/PINS_dataset/PINS'

# List directories
for folder_name in os.listdir(extract_path):
    print(f"Folder: {folder_name}")

Folder: pins_Aaron Paul
Folder: pins_alexandra daddario
Folder: pins_Alvaro Morte
Folder: pins_alycia debnam carey face
Folder: pins_Amanda Crew
Folder: pins_Amaury Nolasco
Folder: pins_amber heard face
Folder: pins_Anna Gunn
Folder: pins_anne hathaway
Folder: pins_barbara palvin face
Folder: pins_bellamy blake face
Folder: pins_Benedict Cumberbatch
Folder: pins_Betsy Brandt
Folder: pins_bill gates
Folder: pins_Brenton Thwaites
Folder: pins_brie larson
Folder: pins_Brit Marling
Folder: pins_Bryan Cranston
Folder: pins_Caity Lotz
Folder: pins_Cameron Monaghan
Folder: pins_chadwick boseman face
Folder: pins_Chance Perdomo
Folder: pins_Chris Evans
Folder: pins_Chris Pratt
Folder: pins_Cobie Smulders
Folder: pins_Danielle Panabaker
Folder: pins_Dave Franco
Folder: pins_david mazouz
Folder: pins_Dominic Purcell
Folder: pins_drake
Folder: pins_dua lipa face
Folder: pins_Dwayne Johnson
Folder: pins_eliza taylor
Folder: pins_elizabeth olsen face
Folder: pins_elon musk
Folder: pins_Emilia Clarke
Folder: pins_Emily Bett Rickards
Folder: pins_Emma Stone
Folder: pins_emma watson face
Folder: pins_gal gadot face
Folder: pins_grant gustin face
Folder: pins_Gwyneth Paltrow
Folder: pins_Henry Cavil
Folder: pins_jason isaacs
Folder: pins_Jason Momoa
Folder: pins_jeff bezos
Folder: pins_Jeremy Renner
Folder: pins_Jesse Eisenberg
Folder: pins_Jim Parsons
Folder: pins_Jon Bernthal
Folder: pins_Josh Radnor
Folder: pins_kiernan shipka
Folder: pins_Kit Harington
Folder: pins_kristen stewart face
Folder: pins_Krysten Ritter
Folder: pins_Kumail Nanjiani
Folder: pins_lindsey morgan face
Folder: pins_Maisie Williams
Folder: pins_margot robbie face
Folder: pins_maria pedraza
Folder: pins_Mark Ruffalo
Folder: pins_mark zuckerberg
Folder: pins_Martin Starr
Folder: pins_Melissa benoit
Folder: pins_miguel herran
Folder: pins_Mike Colter
Folder: pins_millie bobby brown
Folder: pins_Morena Baccarin
Folder: pins_Morgan Freeman
Folder: pins_Natalie Portman
Folder: pins_Neil Patrick Harris
Folder: pins_Paul Rudd
Folder: pins_Pedro Alonso
Folder: pins_Peter Dinklage
Folder: pins_Rami Melek
Folder: pins_rihanna
Folder: pins_RJ Mitte
Folder: pins_robert downey jr face
Folder: pins_Robert Knepper
Folder: pins_Robin Taylor
Folder: pins_Ryan Reynolds
Folder: pins_Sarah Wayne Callies
Folder: pins_Scarlett Johansson
Folder: pins_sean pertwee
Folder: pins_Sebastian Stan
Folder: pins_selena gomez
Folder: pins_shakira
Folder: pins_Sophie Turner
Folder: pins_Stephen Amell
Folder: pins_Sundar Pichai
Folder: pins_tati gabrielle
Folder: pins_taylor swift
Folder: pins_Thomas Middleditch
Folder: pins_Tom Cavanagh
Folder: pins_tom holland face
Folder: pins_Ursula Corbero
Folder: pins_Wentworth Miller
Folder: pins_Willa Holland
Folder: pins_William Fichtner
Folder: pins_zendaya
Folder: PINS

Step 2: Read Images and Extract Labels

In [52]:
#1. Load Images and Labels
import cv2

# Initialize data containers
image_paths = []
labels = []

# Read images and labels
for folder_name in os.listdir(extract_path):
    folder_path = os.path.join(extract_path, folder_name)
    if os.path.isdir(folder_path):  # Ensure it's a folder
        for filename in os.listdir(folder_path):
            file_path = os.path.join(folder_path, filename)
            if filename.endswith(('.jpg', '.png', '.jpeg')):  # Check for image files
                image_paths.append(file_path)
                labels.append(folder_name)  # Use folder name as label
In [53]:
#2. Verify Loaded Data
print(f"Total Images: {len(image_paths)}")
print(f"First 5 Image Paths: {image_paths[:5]}")
print(f"First 5 Labels: {labels[:5]}")

Total Images: 10770
First 5 Image Paths: ['/content/drive/MyDrive/FaceRecognition/PINS/PINS_dataset/PINS/pins_Aaron Paul/Aaron Paul0_262.jpg', '/content/drive/MyDrive/FaceRecognition/PINS/PINS_dataset/PINS/pins_Aaron Paul/Aaron Paul101_247.jpg', '/content/drive/MyDrive/FaceRecognition/PINS/PINS_dataset/PINS/pins_Aaron Paul/Aaron Paul101_248.jpg', '/content/drive/MyDrive/FaceRecognition/PINS/PINS_dataset/PINS/pins_Aaron Paul/Aaron Paul101_249.jpg', '/content/drive/MyDrive/FaceRecognition/PINS/PINS_dataset/PINS/pins_Aaron Paul/Aaron Paul103_217.jpg']
First 5 Labels: ['pins_Aaron Paul', 'pins_Aaron Paul', 'pins_Aaron Paul', 'pins_Aaron Paul', 'pins_Aaron Paul']

Step 3: Save the Data

In [54]:
# Create a DataFrame
data = pd.DataFrame({
    'image_path': image_paths,
    'label': labels
})

# Save to CSV for future use
data_csv = 'PINS_image_data.csv'
data.to_csv(data_csv, index=False)
print(f"Data saved to: {data_csv}")

Data saved to: PINS_image_data.csv

Part II: Visualizing Similar Images

In [55]:
from tensorflow.keras.applications import VGG16
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.models import Model
import cv2
import numpy as np

# Load pre-trained VGG16 model without the top layer
base_model = VGG16(weights='imagenet', include_top=False, pooling='avg')
embedding_model = Model(inputs=base_model.input, outputs=base_model.output)

# Generate embedding vectors for all images
def extract_embeddings_vgg(image_paths, target_size=(224, 224)):
    embeddings = []
    for path in image_paths:
        # Load and preprocess image
        img = cv2.imread(path)
        if img is None:
            print(f"Failed to load image at {path}")
            continue
        img = cv2.resize(img, target_size)
        img = preprocess_input(img)
        img = np.expand_dims(img, axis=0)

        # Extract embedding
        embedding = embedding_model.predict(img)
        embeddings.append(embedding.flatten())
    return np.array(embeddings)

# Validate image paths
valid_paths = [path for path in data['image_path'] if cv2.imread(path) is not None]

# Generate embeddings
if len(valid_paths) == 0:
    print("No valid images found.")
else:
    embeddings = extract_embeddings_vgg(valid_paths)
    print(f"Embeddings Shape: {embeddings.shape}")

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Embeddings Shape: (10770, 512)
In [56]:
#2. Choose a Distance Metric
from sklearn.metrics.pairwise import cosine_similarity

# Compute pairwise similarity
similarity_matrix = cosine_similarity(embeddings)

# Print a sample similarity score
print(f"Sample Similarity Score: {similarity_matrix[0, 1]}")

Sample Similarity Score: 0.49841049313545227
In [57]:
#3. Define a Threshold for Similarity
# Set a similarity threshold
threshold = 0.8

# Identify similar and dissimilar image pairs
similar_pairs = []
dissimilar_pairs = []

for i in range(len(similarity_matrix)):
    for j in range(i + 1, len(similarity_matrix)):  # Avoid duplicates and self-comparison
        if similarity_matrix[i, j] > threshold:
            similar_pairs.append((i, j, similarity_matrix[i, j]))
        elif similarity_matrix[i, j] < 0.4:  # Threshold for dissimilarity
            dissimilar_pairs.append((i, j, similarity_matrix[i, j]))

print(f"Similar Pairs: {len(similar_pairs)}")
print(f"Dissimilar Pairs: {len(dissimilar_pairs)}")

Similar Pairs: 13487897
Dissimilar Pairs: 2283571
In [58]:
#4. Visualize Similar and Dissimilar Images
import matplotlib.pyplot as plt

def visualize_image_pair(idx1, idx2, similarity, title=""):
    img1 = cv2.imread(data['image_path'][idx1])
    img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)

    img2 = cv2.imread(data['image_path'][idx2])
    img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)

    plt.figure(figsize=(10, 5))

    plt.subplot(1, 2, 1)
    plt.imshow(img1)
    plt.title(f"Image 1")
    plt.axis('off')

    plt.subplot(1, 2, 2)
    plt.imshow(img2)
    plt.title(f"Image 2\nSimilarity: {similarity:.2f}")
    plt.axis('off')

    plt.suptitle(title)
    plt.show()

# Visualize 5 similar pairs
print("Similar Images:")
for idx1, idx2, sim in similar_pairs[:5]:
    visualize_image_pair(idx1, idx2, sim, title="Similar Images")

# Visualize 5 dissimilar pairs
print("Dissimilar Images:")
for idx1, idx2, sim in dissimilar_pairs[:5]:
    visualize_image_pair(idx1, idx2, sim, title="Dissimilar Images")

Similar Images:
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Dissimilar Images:
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Part C: To build a face recognition system

Step 1: Apply PCA on Embedding Vectors

In [60]:
# Apply PCA to Reduce Dimensionality
from sklearn.decomposition import PCA

# Apply PCA
pca = PCA(n_components=100)  # Reduce to 100 dimensions (adjustable)
reduced_embeddings = pca.fit_transform(embeddings)
print(f"Reduced Embedding Shape: {reduced_embeddings.shape}")

Reduced Embedding Shape: (10770, 100)

Step 2: Build and Train an SVM Classifier

In [61]:
#1. Prepare the Data

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder

# Encode labels as integers
label_encoder = LabelEncoder()
encoded_labels = label_encoder.fit_transform(data['label'])

# Split into train and test
X_train, X_test, y_train, y_test = train_test_split(reduced_embeddings, encoded_labels, test_size=0.2, random_state=42)
print(f"Train Shape: {X_train.shape}, Test Shape: {X_test.shape}")

Train Shape: (8616, 100), Test Shape: (2154, 100)
In [62]:
#2. Train an SVM Classifier
from sklearn.svm import SVC

# Train the SVM classifier
svm = SVC(kernel='linear', C=1.0, probability=True)  # Linear kernel
svm.fit(X_train, y_train)

# Evaluate the model
train_accuracy = svm.score(X_train, y_train)
test_accuracy = svm.score(X_test, y_test)

print(f"Train Accuracy: {train_accuracy:.2f}")
print(f"Test Accuracy: {test_accuracy:.2f}")

Train Accuracy: 1.00
Test Accuracy: 0.38

Step 3: Use the Trained SVM Model for Inference

In [63]:
#1. Predict Test Labels
# Predict labels
predicted_labels = svm.predict(X_test)

# Decode predicted labels
decoded_predictions = label_encoder.inverse_transform(predicted_labels)
decoded_actual = label_encoder.inverse_transform(y_test)

# Print first 5 predictions
for i in range(5):
    print(f"Predicted: {decoded_predictions[i]}, Actual: {decoded_actual[i]}")

Predicted: pins_Sarah Wayne Callies, Actual: pins_Sarah Wayne Callies
Predicted: pins_Brenton Thwaites, Actual: pins_Brenton Thwaites
Predicted: pins_zendaya, Actual: pins_Cobie Smulders
Predicted: pins_Stephen Amell, Actual: pins_Wentworth Miller
Predicted: pins_millie bobby brown, Actual: pins_millie bobby brown

Insights

  1. Correct Predictions

Example: Predicted: pins_Sarah Wayne Callies, Actual: pins_Sarah Wayne Callies Predicted: pins_Brenton Thwaites, Actual: pins_Brenton Thwaites Predicted: pins_millie bobby brown, Actual: pins_millie bobby brown These indicate that the model successfully identified these individuals. For these cases:

Strengths: The embeddings and classifier work well for these images, suggesting clear and consistent patterns in the features. Possible Reason: High-quality or distinct images, accurate labeling, or sufficient representation in the training dataset.

  1. Incorrect Predictions

Example: Predicted: pins_zendaya, Actual: pins_Cobie Smulders Predicted: pins_Stephen Amell, Actual: pins_Wentworth Miller These cases highlight areas where the model failed to correctly identify the individual.

Reasons for Incorrect Predictions:

Similarity in Appearance:

Celebrities like Stephen Amell and Wentworth Miller may have similar facial features, hairstyles, or expressions, confusing the model. Limited Training Data:

If certain classes (e.g., Cobie Smulders) have fewer images in the training set, the model may not generalize well to unseen test data. Quality of Test Images:

Poor lighting, low resolution, or occlusions (e.g., glasses, hats) may reduce accuracy. Model Limitation:

The model might lack the capacity to distinguish between very similar classes, especially if embeddings overlap in feature space.

In [65]:
#2. Visualize Predictions
def visualize_predictions(image_paths, predicted_labels, actual_labels, num_samples=5, data=data):  # Pass 'data' to the function
    for i in range(num_samples):
        # Get the actual index from y_test
        index = y_test[i]
        # Access the image path using .iloc[] for integer-location based indexing
        img_path = data['image_path'].iloc[index]
        img = cv2.imread(img_path)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

        plt.imshow(img)
        plt.title(f"Predicted: {predicted_labels[i]} | Actual: {actual_labels[i]}")
        plt.axis('off')
        plt.show()

# Visualize predictions
visualize_predictions(data['image_path'], decoded_predictions, decoded_actual)
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Insight:

The model performs well for some individuals (pins_Sarah Wayne Callies, pins_Brenton Thwaites, pins_millie bobby brown) but struggles with others (pins_zendaya, pins_Stephen Amell).

Recommendations- Focus on High-Priority Use Cases:

Identify the most critical individuals for the business (e.g., high-profile celebrities or key customers) and ensure these are prioritized during model improvement. Example: For a celebrity recognition system, invest in improving accuracy for individuals frequently featured in marketing campaigns.

Data-Driven Improvement:

Collect additional high-quality images for underperforming classes like pins_Cobie Smulders and pins_Wentworth Miller. Conduct targeted data augmentation (e.g., varying lighting, poses) to enhance representation.

Misclassifications (pins_zendaya → pins_Cobie Smulders) suggest the need for more robust systems to handle overlapping features or ambiguity.

Adaptive Learning:

Implement a feedback loop where misclassifications are reviewed and used to retrain the model. Example: After identifying an error like pins_Stephen Amell misclassified as pins_Wentworth Miller, update the dataset with more distinguishing images.

Explainable AI:

Provide reasoning behind predictions to increase trust in the system. Example: Show which facial features (eyes, jawline) contributed most to the decision for improved user acceptance.

Summary of Recommendations

Leverage Correct Predictions

    Deploy for high-confidence matches and promote recognized actors in personalized marketing.

Address Misclassifications

    Improve training data, add manual review for ambiguous cases, and use metadata to validate predictions.

Confidence-Based Feedback

    Show confidence levels, offer alternative matches, and educate users about system limitations.

Optimize Performance

   Precompute scene-specific embeddings, use lightweight models, and process paused frames in the background.

User Engagement

   Enable feedback on misclassifications, provide contextual recommendations, and enhance engagement with trivia.

SUBMITTED BY - SASWATI GUPTA¶